In this section, we evaluate the fidelity of the explanations with three fidelity metrics. The first, Deletion [1]}, assumes that the more faithful an explanation is, the quicker the prediction score should drop when pixels that are considered important are shut down. The second one, Insertion [1]}, instead adds pixels on a baseline image, starting with pixels that are associated with the highest importance scores of the explanation. Finally, \(\mu \) Fidelity [3]} creates random pixels subsets which are assigned a baseline value and measures the correlation between the drop in the score and the importance of the explanation.
